Read Data
clean_game <- read.csv("/Users/jennyli/Desktop/VGR/video-game-reviews/clean_data/clean_data_model.csv")
#summary statistics
summary(clean_game$user_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.0 63.0 73.0 69.8 80.0 97.0
summary(clean_game$meta_reviews)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 11.00 18.00 23.94 31.00 126.00
summary(clean_game$user_reviews)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3 14 34 204 107 156973
summary(clean_game$meta_score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 17.00 64.00 73.00 71.13 80.00 99.00
summary(clean_game$years_since_released)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1615 4.7474 10.0931 10.2569 15.3422 26.8118
#Most Common
rank_platform <- sort (table(clean_game$platform), decreasing = TRUE)
#Top 5 platforms
head(rank_platform)
##
## PC PlayStation 4 Xbox 360 PlayStation 2 Switch
## 4358 1840 1449 1189 1149
## PlayStation 3
## 1143
#PC, PlayStation4, Xbox 360,PlayStation2, Switch, and PlayStation3
rank_publisher<- sort (table(clean_game$publishers), decreasing = TRUE)
#Top 5 publisher
head(rank_publisher)
##
## Ubisoft Electronic Arts Activision Nintendo Sega
## 726 660 624 551 540
## Capcom
## 460
#Ubisoft, Electronic Arts, Activision, Nintendo, Sega, and Capcom
rank_developer <- sort (table(clean_game$developers), decreasing = TRUE)
#Top 5 developer
head(rank_developer)
##
## Capcom Telltale Games EA Sports EA Canada
## 280 217 176 155
## Konami Ubisoft Montreal
## 148 139
# Capcom, Telltale games, EA Sports, EA Canada, Knami, Ubisoft Montreal
#Distributions/ Histogram of scores
#user score
hist(clean_game$user_score)
#meta score
hist(clean_game$meta_score)
#Visualizing the difference between meta score and user score
clean_game$score_diff <- clean_game$meta_score -clean_game$user_score
hist(clean_game$score_diff)
plot_ly(data = clean_game, type = "scatter", mode = "markers", x = ~user_score, y = ~meta_score)
## Warning: `arrange_()` was deprecated in dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
#color by??
# Correlations for numeric columns
clean_game_cor <-select(clean_game,user_score, user_reviews, meta_score, meta_reviews, best_game, most_discussed, most_shared, score_diff, years_since_released)
M <-cor(clean_game_cor)
corrplot(M, type="upper", order="hclust",
col=brewer.pal(n=8, name="RdYlBu"))
#Frequency of ESRB Ratings and Pie plot
table(clean_game$esrb_ratings)
##
## AO E E10+ K-A M missing RP T
## 3 3934 2194 6 3427 1730 17 4951
prop = table(clean_game$esrb_ratings)/nrow(clean_game)
style = names(prop)
plot_ly() %>%
add_trace(type = "pie", labels = ~style, values = ~prop, textinfo = "percent")
#Visualization of score difference for the top 5 platform
clean_game_5platform <- filter (clean_game, platform == "PC"| platform == "PlayStation 4"|platform == "Xbox 360"| platform == "PlayStation 2"|platform == "PlayStation 3")
#plot_ly(data = clean_game_5platform, type = "scatter", mode = "markers", x = ~user_score, y = ~meta_score, color = ~platform)
#plot_ly(data = clean_game_5platform) %>%
#add_trace(type = "scatter", x = ~user_score, y = ~meta_score, color = ~platform) #%>%
# add_lines( x = ~user_score, y = ~meta_score, color = ~platform)
ggplot(data=clean_game_5platform) +
geom_point(mapping = aes(x=user_score, y=meta_score, color=platform)) +
geom_smooth(mapping = aes(x=user_score, y=meta_score, color=platform),
method=lm, se=FALSE, fullrange=TRUE)
## `geom_smooth()` using formula 'y ~ x'
#Frequency of each esrb content descriptor
esrb_freq <-list("Blood",sum(clean_game$esrb_descs_Blood),
"Gambling", sum(clean_game$esrb_descs_Gambling),
"Humor", sum(clean_game$esrb_descs_Humor),
"Language", sum(clean_game$esrb_descs_Language),
"Nudity", sum(clean_game$esrb_descs_Nudity),
"Violence", sum(clean_game$esrb_descs_Violence),
"Missing", sum(clean_game$esrb_descs_missing))
## Violence 6081, Blood 2790, Language 2422
#Frequency for genre
genre_freq <-matrix (c (sum(clean_game$genres_2D),
sum(clean_game$genres_3D),
sum(clean_game$genres_4X),
sum(clean_game$genres_Action),
sum(clean_game$genres_Action.Adventure),
sum(clean_game$genres_Action.RPG),
sum(clean_game$genres_Adventure),
sum(clean_game$genres_Alternative),
sum(clean_game$genres_Application),
sum(clean_game$genres_Arcade),
sum(clean_game$genres_Artillery),
sum(clean_game$genres_Athletics),
sum(clean_game$genres_Automobile),
sum(clean_game$genres_Baseball),
sum(clean_game$genres_Basketball),
sum(clean_game$genres_Beat..Em.Up),
sum(clean_game$genres_Biking),
sum(clean_game$genres_Billiards),
sum(clean_game$genres_Board...Card.Game),
sum(clean_game$genres_Board.Games),
sum(clean_game$genres_Bowling),
sum(clean_game$genres_Boxing),
sum(clean_game$genres_Boxing...Martial.Arts),
sum(clean_game$genres_Breeding.Constructing),
sum(clean_game$genres_Business...Tycoon),
sum(clean_game$genres_Car.Combat),
sum(clean_game$genres_Card.Battle),
sum(clean_game$genres_Career),
sum(clean_game$genres_City.Building),
sum(clean_game$genres_Civilian),
sum(clean_game$genres_Civilian.Plane),
sum(clean_game$genres_Combat),
sum(clean_game$genres_Command),
sum(clean_game$genres_Compilation),
sum(clean_game$genres_Console.style.RPG),
sum(clean_game$genres_Cricket),
sum(clean_game$genres_Dancing),
sum(clean_game$genres_Defense),
sum(clean_game$genres_Demolition.Derby),
sum(clean_game$genres_Drag),
sum(clean_game$genres_Driving),
sum(clean_game$genres_Edutainment),
sum(clean_game$genres_Exercise...Fitness),
sum(clean_game$genres_Fantasy),
sum(clean_game$genres_Fighting),
sum(clean_game$genres_First.Person),
sum(clean_game$genres_Fishing),
sum(clean_game$genres_Flight),
sum(clean_game$genres_Football),
sum(clean_game$genres_Formula.One),
sum(clean_game$genres_Futuristic),
sum(clean_game$genres_Futuristic.Jet),
sum(clean_game$genres_Futuristic.Sub),
sum(clean_game$genres_Gambling),
sum(clean_game$genres_General),
sum(clean_game$genres_Golf),
sum(clean_game$genres_Government),
sum(clean_game$genres_GT...Street),
sum(clean_game$genres_Helicopter),
sum(clean_game$genres_Hidden.Object),
sum(clean_game$genres_Historic),
sum(clean_game$genres_Horizontal),
sum(clean_game$genres_Horror),
sum(clean_game$genres_Horse.Racing),
sum(clean_game$genres_Hunting),
sum(clean_game$genres_Ice.Hockey),
sum(clean_game$genres_Individual),
sum(clean_game$genres_Interactive.Movie),
sum(clean_game$genres_Japanese.Style),
sum(clean_game$genres_Kart),
sum(clean_game$genres_Large.Spaceship),
sum(clean_game$genres_Light.Gun),
sum(clean_game$genres_Linear),
sum(clean_game$genres_Logic),
sum(clean_game$genres_Management),
sum(clean_game$genres_Marine),
sum(clean_game$genres_Massively.Multiplayer),
sum(clean_game$genres_Massively.Multiplayer.Online),
sum(clean_game$genres_Matching),
sum(clean_game$genres_Mech),
sum(clean_game$genres_Military),
sum(clean_game$genres_Miscellaneous),
sum(clean_game$genres_Mission.based),
sum(clean_game$genres_MOBA),
sum(clean_game$genres_Modern),
sum(clean_game$genres_Modern.Jet),
sum(clean_game$genres_Motocross),
sum(clean_game$genres_Motorcycle),
sum(clean_game$genres_Music),
sum(clean_game$genres_Music.Maker),
sum(clean_game$genres_Nature),
sum(clean_game$genres_Olympic.Sports),
sum(clean_game$genres_On.foot),
sum(clean_game$genres_Open.World),
sum(clean_game$genres_Other),
sum(clean_game$genres_Parlor),
sum(clean_game$genres_Party),
sum(clean_game$genres_Party...Minigame),
sum(clean_game$genres_PC.style.RPG),
sum(clean_game$genres_Pet),
sum(clean_game$genres_Pinball),
sum(clean_game$genres_Platformer),
sum(clean_game$genres_Point.and.Click),
sum(clean_game$genres_Puzzle),
sum(clean_game$genres_Racing),
sum(clean_game$genres_Rail),
sum(clean_game$genres_Rally...Offroad),
sum(clean_game$genres_Real.Time),
sum(clean_game$genres_Rhythm),
sum(clean_game$genres_Roguelike),
sum(clean_game$genres_Role.Playing),
sum(clean_game$genres_Rugby),
sum(clean_game$genres_Sandbox),
sum(clean_game$genres_Sci.Fi),
sum(clean_game$genres_Scrolling),
sum(clean_game$genres_Ship),
sum(clean_game$genres_Shoot..Em.Up),
sum(clean_game$genres_Shooter),
sum(clean_game$genres_Sim),
sum(clean_game$genres_Simulation),
sum(clean_game$genres_Skate...Skateboard),
sum(clean_game$genres_Skateboarding),
sum(clean_game$genres_Skating),
sum(clean_game$genres_Ski...Snowboard),
sum(clean_game$genres_Skiing),
sum(clean_game$genres_Small.Spaceship),
sum(clean_game$genres_Snow...Water),
sum(clean_game$genres_Snowboarding),
sum(clean_game$genres_Soccer),
sum(clean_game$genres_Space),
sum(clean_game$genres_Sports),
sum(clean_game$genres_Stacking),
sum(clean_game$genres_Static),
sum(clean_game$genres_Stock.Car),
sum(clean_game$genres_Strategy),
sum(clean_game$genres_Street),
sum(clean_game$genres_Submarine),
sum(clean_game$genres_Surf...Wakeboard),
sum(clean_game$genres_Surfing),
sum(clean_game$genres_Survival),
sum(clean_game$genres_Tactical),
sum(clean_game$genres_Tactics),
sum(clean_game$genres_Tank),
sum(clean_game$genres_Team),
sum(clean_game$genres_Tennis),
sum(clean_game$genres_Text),
sum(clean_game$genres_Third.Person),
sum(clean_game$genres_Top.Down),
sum(clean_game$genres_Traditional),
sum(clean_game$genres_Train),
sum(clean_game$genres_Trainer),
sum(clean_game$genres_Trivia...Game.Show),
sum(clean_game$genres_Truck),
sum(clean_game$genres_Turn.Based),
sum(clean_game$genres_Tycoon),
sum(clean_game$genres_Vehicle),
sum(clean_game$genres_Vertical),
sum(clean_game$genres_Videos),
sum(clean_game$genres_Virtual),
sum(clean_game$genres_Virtual.Life),
sum(clean_game$genres_Visual.Novel),
sum(clean_game$genres_Volleyball),
sum(clean_game$genres_Wakeboarding),
sum(clean_game$genres_Wargame),
sum(clean_game$genres_Western.Style),
sum(clean_game$genres_Wrestling),
sum(clean_game$genres_WWI),
sum(clean_game$genres_WWII)
), ncol = 1)
colnames(genre_freq) <- c('frequency')
rownames(genre_freq) <- c('2D','3D','4X','Action','Action.Adventure','Action.RPG',
'Adventure','Alternative','Application','Arcade','Artillery','Athletics','Automobile','Baseball',
'Basketball','Beat..Em.Up','Biking','Billiards','Board...Card.Game','Board.Games','Bowling',
'Boxing','Boxing...Martial.Arts','Breeding.Constructing','Business...Tycoon','Car.Combat',
'Card.Battle','Career','City.Building','Civilian','Civilian.Plane','Combat','Command',
'Compilation','Console.style.RPG','Cricket','Dancing','Defense','Demolition.Derby','Drag',
'Driving','Edutainment','Exercise...Fitness','Fantasy','Fighting','First.Person','Fishing',
'Flight','Football','Formula.One','Futuristic','Futuristic.Jet','Futuristic.Sub','Gambling',
'General','Golf','Government','GT...Street','Helicopter','Hidden.Object','Historic','Horizontal',
'Horror','Horse.Racing','Hunting','Ice.Hockey','Individual','Interactive.Movie','Japanese.Style',
'Kart','Large.Spaceship','Light.Gun','Linear','Logic','Management','Marine',
'Massively.Multiplayer','Massively.Multiplayer.Online','Matching','Mech','Military',
'Miscellaneous','Mission.based','MOBA','Modern','Modern.Jet','Motocross','Motorcycle','Music',
'Music.Maker','Nature','Olympic.Sports','On.foot','Open.World','Other','Parlor','Party',
'Party...Minigame','PC.style.RPG','Pet','Pinball','Platformer','Point.and.Click','Puzzle',
'Racing','Rail','Rally...Offroad','Real.Time','Rhythm','Roguelike','Role.Playing','Rugby',
'Sandbox','Sci.Fi','Scrolling','Ship','Shoot..Em.Up','Shooter','Sim','Simulation',
'Skate...Skateboard','Skateboarding','Skating','Ski...Snowboard','Skiing','Small.Spaceship',
'Snow...Water','Snowboarding','Soccer','Space','Sports','Stacking','Static','Stock.Car',
'Strategy','Street','Submarine','Surf...Wakeboard','Surfing','Survival','Tactical','Tactics',
'Tank','Team','Tennis','Text','Third.Person','Top.Down','Traditional','Train','Trainer',
'Trivia...Game.Show','Truck','Turn.Based','Tycoon','Vehicle','Vertical','Videos','Virtual',
'Virtual.Life','Visual.Novel','Volleyball','Wakeboarding','Wargame','Western.Style','Wrestling',
'WWI','WWII')
## common genre:action, general, action adventure, shooter, and role player
#Adding the variable "year" for detecting/visualizing trend
clean_game$time <- clean_game$release_date
clean_game <- separate(data = clean_game, col = time, into = c("year", "rest"), sep = 4)
#Detecting trends of score difference based on platform over the year
game_by_platform <- group_by(clean_game, platform, year)
platform_year_score <- summarize (game_by_platform,
mean_user = mean (user_score, na.rm = TRUE),
mean_meta = mean (meta_score, na.rm = TRUE))
## `summarise()` has grouped output by 'platform'. You can override using the `.groups` argument.
platform_year_score$mean_diff <- platform_year_score$mean_meta - platform_year_score$mean_user
tidy_plt_yr_score = subset(platform_year_score, select = -c(mean_user,mean_meta) )
tidy_plt_yr_score <- gather (data = tidy_plt_yr_score, key = description, value = value, mean_diff)
plot_ly(data = tidy_plt_yr_score, type = "bar",
x = ~fct_reorder(platform, value), y = ~value, frame = ~year, showlegend = FALSE)%>%
animation_opts(frame = 3000, easing = "elastic", redraw = FALSE)
# Visualization of trends of esrb descriptors of the year
game_by_year <- group_by (clean_game, year)
year_desc <- summarize (game_by_year,
gambling = sum(esrb_descs_Gambling, na.rm = TRUE),
blood = sum(esrb_descs_Blood, na.rm = TRUE),
language= sum(esrb_descs_Language, na.rm = TRUE),
humor = sum(esrb_descs_Humor, na.rm = TRUE),
nudity = sum(esrb_descs_Nudity, na.rm = TRUE),
violence = sum(esrb_descs_Violence, na.rm = TRUE),
missing = sum(esrb_descs_missing, na.rm = TRUE))
tidy_year_desc <- gather (data = year_desc, key = descriptor, value = number, gambling, blood, language, humor, nudity, violence, missing)
plot_ly(data = tidy_year_desc, type = "bar",
x = ~fct_reorder(descriptor, number), y = ~number, frame = ~year, showlegend = FALSE)%>%
animation_opts(frame = 1000, easing = "elastic", redraw = FALSE)
#double check about missing esrb descriptions after 2015
More work to do:
1. missing descriptors after 2015
2. how to deal with genres and detect trends